Team
We are computational chemists, structural biologists, and ML researchers. Everyone on this team has a publication record in drug discovery — not just in machine learning.
CEO & Co-Founder
Computational chemist with ten years in structure-based drug design — first at a Boston-area biotech focused on kinase inhibitors, then as a postdoc in the Shoichet Lab at UCSF where he worked on fragment-based screening and free-energy scoring. Returned to Cambridge in 2023 to rebuild the target-to-lead stack from first principles. Designed and validated the GNN binding affinity core.
Co-Founder & Head of Computational Chemistry
Spent eight years in the ADME/tox computational group at a mid-size Cambridge biotech, building QSAR models for metabolic stability, CYP inhibition, and reactive metabolite flagging. Joined Manas AI as co-founder to extend that work across all 48 endpoints and integrate it into the unified pipeline. Holds a PhD in pharmaceutical chemistry from the University of Michigan.
ML Research Lead
PhD in computational biology from MIT; postdoctoral work at the Broad Institute on heterogeneous graph representations of protein-ligand complexes. His thesis work on message-passing architectures for binding affinity prediction is the foundation of Manas AI's GNN scorer. Leads model architecture, training data curation, and uncertainty quantification.
Structural Biology Lead
Protein crystallographer with a background in X-ray diffraction and single-particle cryo-EM. Previously at a structural biology CRO in Germany before moving to Cambridge. At Manas AI, she audits the structural inputs to the docking pipeline — identifying resolution artefacts, missing loops, and binding-site waters that mislead scoring functions — and manages relationships with the wet labs that run our prospective validation assays.
Advisory Board